Characterization and functional consequences of underexpression of clusterin in rheumatoid arthritis

We previously compared by microarray analysis gene expression in rheumatoid arthritis (RA) and osteoarthritis (OA) tissues. Among the set of genes identified as a molecular signature of RA, clusterin (clu) was one of the most differentially expressed. In the present study we sought to assess the expression and the role of CLU (mRNA and protein) in the affected joints and in cultured fibroblast-like synoviocytes (FLS) and to determine its functional role. Quantitative RT-PCR, Northern blot, in situ hybridization, immunohistochemistry, and Western blot were used to specify and quantify the expression of CLU in ex vivo synovial tissue. In synovial tissue, the protein was predominantly expressed by synoviocytes and it was detected in synovial fluids. Both full-length and spliced isoform CLU mRNA levels of expression were lower in RA tissues compared with OA and healthy synovium. In synovium and in cultured FLS, the overexpression of CLU concerned all protein isoforms in OA whereas in RA, the intracellular forms of the protein were barely detectable. Transgenic overexpression of CLU in RA FLS promoted apoptosis within 24 h. We observed that CLU knockdown with small interfering RNA promoted IL-6 and IL-8 production. CLU interacted with phosphorylated IkappaBalpha. Differential expression of CLU by OA and RA FLS appeared to be an intrinsic property of the cells. Expression of intracellular isoforms of CLU is differentially regulated between OA and RA. We propose that in RA joints, high levels of extracellular CLU and low expression of intracellular CLU may enhance NF-kappaB activation and survival of the synoviocytes.     

Identification of blood biomarkers of rheumatoid arthritis by transcript profiling of peripheral blood mononuclear cells from the rat collagen-induced arthritis model

Rheumatoid arthritis (RA) is a chronic debilitating autoimmune disease that results in joint destruction and subsequent loss of function. To better understand its pathogenesis and to facilitate the search for novel RA therapeutics, we profiled the rat model of collagen-induced arthritis (CIA) to discover and characterize blood biomarkers for RA. Peripheral blood mononuclear cells (PBMCs) were purified using a Ficoll gradient at various time points after type II collagen immunization for RNA preparation. Total RNA was processed for a microarray analysis using Affymetrix GeneChip technology. Statistical comparison analyses identified differentially expressed genes that distinguished CIA from control rats. Clustering analyses indicated that gene expression patterns correlated with laboratory indices of disease progression. A set of 28 probe sets showed significant differences in expression between blood from arthritic rats and that from controls at the earliest time after induction, and the difference persisted for the entire time course. Gene Ontology comparison of the present study with previous published murine microarray studies showed conserved Biological Processes during disease induction between the local joint and PBMC responses. Genes known to be involved in autoimmune response and arthritis, such as those encoding Galectin-3, Versican, and Socs3, were identified and validated by quantitative TaqMan RT-PCR analysis using independent blood samples. Finally, immunoblot analysis confirmed that Galectin-3 was secreted over time in plasma as well as in supernatant of cultured tissue synoviocytes of the arthritic rats, which is consistent with disease progression. Our data indicate that gene expression in PBMCs from the CIA model can be utilized to identify candidate blood biomarkers for RA.     

Differential Gene Expression in Synovium of Rheumatoid Arthritis and Osteoarthritis

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the major types of arthritis. Although both diseases are characterized by joint destruction, their etiologies are different. To get insights into pathophysiological pathways, we used the suppression subtractive hybridization (SSH) method to identify differentially expressed genes in RA. DNA sequencing identified 12 gene products including cytoskeletal γ-actin and extracellular matrix components such as fibronectin, collagen IIIα₁, and superficial zone protein. Interferon γ-inducible genes such as a novel thiol reductase, two genes of unknown function (HSIFNIN4, RING3), and annexin II were also found. Two genes encoded proteins involved in proliferation such as elongation factor 1α and the granulin precursor. Furthermore, the protease cathepsin B and synovial phospholipase A2 group IIA were detected by SSH. To confirm the differential expression of the genes, we performed RT-PCR analyses of RA and OA synovial tissues. Compared to OA patients, 9 of the 12 genes were overexpressed in RA, suggesting that SSH is a powerful tool for the detection of differential gene expression in synovial tissues. Further characterization of the gene products may help to identify pathophysiological mechanisms in arthritic diseases.     

Identification of blood biomarkers of rheumatoid arthritis by transcript profiling of peripheral blood mononuclear cells from the rat collagen-induced arthritis model

Rheumatoid arthritis (RA) is a chronic debilitating autoimmune disease that results in joint destruction and subsequent loss of function. To better understand its pathogenesis and to facilitate the search for novel RA therapeutics, we profiled the rat model of collagen-induced arthritis (CIA) to discover and characterize blood biomarkers for RA. Peripheral blood mononuclear cells (PBMCs) were purified using a Ficoll gradient at various time points after type II collagen immunization for RNA preparation. Total RNA was processed for a microarray analysis using Affymetrix GeneChip technology. Statistical comparison analyses identified differentially expressed genes that distinguished CIA from control rats. Clustering analyses indicated that gene expression patterns correlated with laboratory indices of disease progression. A set of 28 probe sets showed significant differences in expression between blood from arthritic rats and that from controls at the earliest time after induction, and the difference persisted for the entire time course. Gene Ontology comparison of the present study with previous published murine microarray studies showed conserved Biological Processes during disease induction between the local joint and PBMC responses. Genes known to be involved in autoimmune response and arthritis, such as those encoding Galectin-3, Versican, and Socs3, were identified and validated by quantitative TaqMan RT-PCR analysis using independent blood samples. Finally, immunoblot analysis confirmed that Galectin-3 was secreted over time in plasma as well as in supernatant of cultured tissue synoviocytes of the arthritic rats, which is consistent with disease progression. Our data indicate that gene expression in PBMCs from the CIA model can be utilized to identify candidate blood biomarkers for RA.     

表达芯片筛选类风湿关节炎特异表达基因

为了寻找类风湿关节炎（rheumatoid arthritis,RA)新的特异性表达标记物,应用基因芯片对RA、骨关节炎（osteoarthritis,OA）和强直性脊柱炎（ankylosing spondylitis,AS）患者关节滑膜组织的基因表达谱进行比较,并采用实时定量PCR方法对芯片结果进行验证. 全基因组表达芯片的结果显示,与OA滑膜组织及AS滑膜组织相比,在RA患者的滑膜组织中,CD38,ANKRD38,E2F2,CFDP1, CD7, ISG20 和IL2RG基因表达异常|实时定量PCR结果证实,RA患者滑膜组织中,CD38、E2F2和IL2RG基因表达明显增高.     

Identification of differentially expressed genes in synovial tissue of rheumatoid arthritis and osteoarthritis in patients

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the common joints disorder in the world. Although they have showed the analogous clinical manifestation and overlapping cellular and molecular foundation, the pathogenesis of RA and OA were different. The pathophysiologic mechanisms of arthritis in RA and OA have not been investigated thoroughly. Thus, the aim of study is to identify the potential crucial genes and pathways associated with RA and OA and further analyze the molecular mechanisms implicated in genesis. First, we compared gene expression profiles in synovial tissue between RA and OA from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Gene Expression Series (GSE) 1919, GSE55235, and GSE36700 were downloaded from the GEO database, including 20 patients of OA and 21 patients of RA. Differentially expressed genes (DEGs) including “CXCL13,” “CD247,” “CCL5,” “GZMB,” “IGKC,” “IL7R,” “UBD///GABBR1,” “ADAMDEC1,” “BTC,” “AIM2,” “SHANK2,” “CCL18,” “LAMP3,” “CR1,” and “IL32.” Second, Gene Ontology analyses revealed that DEGs were significantly enriched in integral component of extracellular space, extracellular region, and plasma membrane in the molecular function group. Signaling pathway analyses indicated that DEGs had common pathways in chemokine signaling pathway, cytokine-cytokine receptor interaction, and cytosolic DNA-sensing pathway. Third, DEGs showed the complex DEGs protein-protein interaction network with the Coexpression of 83.22%, Shared protein domains of 8.40%, Colocalization of 4.76%, Predicted of 2.87%, and Genetic interactions of 0.75%. In conclusion, the novel DEGs and pathways between RA and OA identified in this study may provide new insight into the underlying molecular mechanisms of RA.     

Variability in synovial inflammation in rheumatoid arthritis investigated by microarray technology

In recent years microarray technology has been used increasingly to acquire knowledge about the pathogenic processes involved in rheumatoid arthritis. The present study investigated variations in gene expression in synovial tissues within and between patients with rheumatoid arthritis. This was done by applying microarray technology on multiple synovial biopsies obtained from the same knee joints. In this way the relative levels of intra-patient and inter-patient variation could be assessed. The biopsies were obtained from 13 different patients: 7 by orthopedic surgery and 6 by rheumatic arthroscopy. The data show that levels of heterogeneity varied substantially between the biopsies, because the number of genes found to be differentially expressed between pairs of biopsies from the same knee ranged from 6 to 2,133. Both arthroscopic and orthopedic biopsies were examined, allowing us to compare the two sampling methods. We found that the average number of differentially expressed genes between biopsies from the same patient was about three times larger in orthopedic than in arthroscopic biopsies. Using a parallel analysis of the tissues by immunohistochemistry, we also identified orthopedic biopsies that were unsuitable for gene expression analysis of synovial inflammation due to sampling of non-inflamed parts of the tissue. Removing these biopsies reduced the average number of differentially expressed genes between the orthopedic biopsies from 455 to 171, in comparison with 143 for the arthroscopic biopsies. Hierarchical clustering analysis showed that the remaining orthopedic and arthroscopic biopsies had gene expression signatures that were unique for each patient, apparently reflecting patient variation rather than tissue heterogeneity. Subsets of genes found to vary between biopsies were investigated for overrepresentation of biological processes by using gene ontology. This revealed representative 'themes' likely to vary between synovial biopsies affected by inflammatory disease.     

Chronically inflamed synovium from spondyloarthropathy and rheumatoid arthritis investigated by protein expression profiling followed by tandem mass spectrometry

We investigated the cytosolic proteome of inflamed synovial tissue by hierarchical clustering analysis and validated the feasibility of this proteome analysis by identifying proteins that were differentially expressed between rheumatoid arthritis (RA), spondyloarthropathy (SpA), and osteoarthritis (OA). Synovial biopsy samples were obtained from 18 patients undergoing needle arthroscopy for knee synovitis associated with RA (n = 6) and SpA (n = 6), and for joint effusion of the knee associated with OA (n = 6). Cytosolic proteins were extracted from the tissue and subjected to two-dimensional gel electrophoresis. Protein expression patterns were statistically analyzed and used for hierarchical cluster analysis. Proteins of interest were independently identified by matrix-assisted laser desorption/ionization- and electrospray ionization-mass spectrometry. Hierarchical cluster analysis of the complete match set, containing 640 spots, remarkably segregated SpA from RA and OA. Next, we used a subset of spots that was statistically, differentially expressed (P < 0.01), between RA and SpA, SpA and OA, or RA and OA, in both Student's t-test and Mann-Whitney U-test. The dendrograms revealed distinct clustering of RA versus SpA and RA versus OA. Spots that were differentially expressed between the groups were identified by tandem mass spectrometry. Fructose bisphosphate aldolase A and alpha-enolase showed higher expression levels in SpA than in OA (P < 0.01). Calgranulin A myeloid related protein-8 (MRP-8) was markedly up-regulated in RA and SpA patients in comparison to OA patients where this spot was below detection limit. The analysis of the cytosolic proteome of synovial tissue is a useful approach to identify disease-associated proteins in chronic inflammatory arthritis.     

Microarray analyses of peripheral blood cells identifies unique gene expression signature in psoriatic arthritis

Psoriatic arthritis (PsA) is a chronic and erosive form of arthritis of unknown cause. We aimed to characterize the PsA phenotype using gene expression profiling and comparing it with healthy control subjects and patients rheumatoid arthritis (RA). Peripheral blood cells (PBCs) of 19 patients with active PsA and 19 age- and sex-matched control subjects were used in the analyses of PsA, with blood samples collected in PaxGene tubes. A significant alteration in the pattern of expression of 313 genes was noted in the PBCs of PsA patients on Affymetrix U133A arrays: 257 genes were expressed at reduced levels in PsA, and 56 genes were expressed at increased levels, compared with controls. Downregulated genes tended to cluster to certain chromosomal regions, including those containing the psoriasis susceptibility loci PSORS1 and PSORS2. Among the genes with the most significantly reduced expression were those involved in downregulation or suppression of innate and acquired immune responses, such as SIGIRR, STAT3, SHP1, IKBKB, IL-11RA, and TCF7, suggesting inappropriate control that favors proin-flammatory responses. Several members of the MAPK signaling pathway and tumor suppressor genes showed reduced expression. Three proinflammatory genes--S100A8, S100A12, and thioredoxin--showed increased expression. Logistic regression and recursive partitioning analysis determined that one gene, nucleoporin 62 kDa, could correctly classify all controls and 94.7% of the PsA patients. Using a dataset of 48 RA samples for comparison, the combination of two genes, MAP3K3 followed by CACNA1S, was enough to correctly classify all RA and PsA patients. Thus, PBC gene expression profiling identified a gene expression signature that differentiated PsA from RA, and PsA from controls. Several novel genes were differentially expressed in PsA and may prove to be diagnostic biomarkers or serve as new targets for the development of therapies.     

Effect of infliximab on mRNA expression profiles in synovial tissue of rheumatoid arthritis patients

We examined the gene expression profiles in arthroscopic biopsies retrieved from 10 rheumatoid arthritis patients before and after anti-TNF treatment with infliximab to investigate whether such profiles can be used to predict responses to the therapy, and to study effects of the therapy on the profiles. Responses to treatment were assessed using European League Against Rheumatism response criteria. Three patients were found to be good responders, five patients to be moderate responders and two patients to be nonresponders. The TNF-alpha status of the biopsies from each of the patients before treatment was also investigated immunohistochemically, and it was detected in biopsies from four of the patients, including all three of the good responders. The gene expression data demonstrate that all patients had unique gene expression signatures, with low intrapatient variability between biopsies. The data also revealed significant differences between the good responding and nonresponding patients (279 differentially expressed genes were detected, with a false discovery rate < 0.025). Among the identified genes we found that MMP-3 was significantly upregulated in good responders (log2 fold change, 2.95) compared with nonresponders, providing further support for the potential of MMP-3 as a marker for good responses to therapy. An even more extensive list of 685 significantly differentially expressed genes was found between patients in whom TNF-alpha was found and nonresponders, indicating that TNF-alpha could be an important biomarker for successful infliximab treatment. Significant differences were also observed between biopsies taken before and after anti-TNF treatment, including 115 differentially expressed genes in the good responding group. Interestingly, the effect was even stronger in the group in which TNF-alpha was immunohistochemically detected before therapy. Here, 1,058 genes were differentially expressed, including many that were novel in this context (for example, CXCL3 and CXCL14). Subsequent Gene Ontology analysis revealed that several 'themes' were significantly over-represented that are known to be affected by anti-TNF treatment in inflammatory tissue; for example, immune response (GO:0006955), cell communication (GO:0007154), signal transduction (GO:0007165) and chemotaxis (GO:0006935). No genes reached statistical significance in the moderately responding or nonresponding groups. In conclusion, this pilot study suggests that further investigation is warranted on the usefulness of gene expression profiling of synovial tissue to predict and monitor the outcome of rheumatoid arthritis therapies.     

Batch correction of microarray data substantially improves the identification of genes differentially expressed in Rheumatoid Arthritis and Osteoarthritis

ABSTRACT: BACKGROUND: Batch effects due to sample preparation or array variation (type, charge, and/or platform) may influence the results of microarray experiments and thus mask and/or confound true biological differences. Of the published approaches for batch correction, the algorithm "Combating Batch Effects When Combining Batches of Gene Expression Microarray Data" (ComBat) appears to be most suitable for small sample sizes and multiple batches. METHODS: Synovial fibroblasts (SFB; purity > 98 %) were obtained from rheumatoid arthritis (RA) and osteoarthritis (OA) patients (n = 6 each) and stimulated with TNF-alpha or TGF-beta1 for 0, 1, 2, 4, or 12 hours. Gene expression was analyzed using Affymetrix Human Genome U133 Plus 2.0 chips, an alternative chip definition file, and normalization by Robust Multi-Array Analysis (RMA). Data were batch-corrected for different acquiry dates using ComBat and the efficacy of the correction was validated using hierarchical clustering. RESULTS: In contrast to the hierarchical clustering dendrogram before batch correction, in which RA and OA patients clustered randomly, batch correction led to a clear separation of RA and OA. Strikingly, this applied not only to the 0 hour time point (i.e., before stimulation with TNF-alpha/TGF-beta1), but also to all time points following stimulation except for the late 12 hour time point. Batch-corrected data then allowed the identification of differentially expressed genes discriminating between RA and OA. Batch correction only marginally modified the original data, as demonstrated by preservation of the main Gene Ontology (GO) categories of interest, and by minimally changed mean expression levels (maximal change 4.087 %) or variances for all genes of interest. Eight genes from the GO category "extracellular matrix structural constituent" (5 different collagens, biglycan, and tubulointerstitial nephritis antigen-like 1) were differentially expressed between RA and OA (RA > OA), both constitutively at time point 0, and at all time points following stimulation with either TNF-alpha or TGF-beta1. CONCLUSIONS: Batch correction appears to be an extremely valuable tool to eliminate non-biological batch effects, and allows the identification of genes discriminating between different joint diseases. RA-SFB show an upregulated expression of extracellular matrix components, both constitutively following isolation from the synovial membrane and upon stimulation with disease-relevant cytokines or growth factors, suggesting an "imprinted" alteration of their phenotype.